Either a $d$ - vector or an $n\times d$ matrix, where $d$
is the dimension of the normal distribution and $n$ is the number of
points at which the density is to be evaluated.
mu
$d$ - vector: Mean of the normal distribution (or NULL uses
the origin as default)
sigma
This $d\times d$ matrix is the variance matrix of the normal
distribution (or NULL uses the identity matrix as default)
Value
dmvnorm gives the densities, while
logdmvnorm gives the logarithm of the densities.
Details
This code is written to be efficient, using the qr-decomposition of the
covariance matrix (and using it only once, rather than recalculating it
for both the determinant and the inverse of sigma).